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A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application

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Abstract

Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse.

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Authors and Affiliations

  1. Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, Guangzhou, 510070, Guangdong, People’s Republic of China

    Xiaoxiao Li, Zhihao Xu & Xuefeng Zhou

  2. School of Engineering, Swansea University, Swansea, UK

    Shuai Li

  3. Foshan Tri-Co Intelligent Robot Technology Company Ltd., Foshan, 528300, People’s Republic of China

    Shuai Li, Zhihao Xu & Xuefeng Zhou

Authors
  1. Xiaoxiao Li
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  2. Shuai Li
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  3. Zhihao Xu
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  4. Xuefeng Zhou
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Correspondence to Xuefeng Zhou.

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This work is supported by Guangdong Province Key Areas R&D Program (Grant No. 2019B090919002), Foshan Key Technology Research Project (Grant No. 1920001001148), Foshan Innovation and Entrepreneurship Team Project (Grant No. 2018IT100173), Guangzhou Science Research Plan-Major Project (Grant No. 201804020095), Guangdong Innovative Talent Project of Young College (Grant No. 2016TQ03X463), GDAS’ Project of Thousand doctors (postdoctors) Introduction (2019GDASYL-0103078), GDAS’ Project of Science and Technology Development (2018GDASCX-0115)

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Li, X., Li, S., Xu, Z. et al. A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application. Neural Process Lett 53, 1287–1304 (2021). https://doi.org/10.1007/s11063-021-10440-x

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